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Scalable Training of L1-regularized Log-linear Models
Galen Andrew
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Oznake: computer science;machine learning;linear models
The l-bfgs limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear models with L2 regularization, but it cannot be used for an L1-regularized loss due to its non-differentiability whenever some parameter is zero. Eficient algorithms have ...
Leto: 2007 Vir: videolectures.net
Št. zadetkov: 1
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